Analysis of Indian Renewable Energy Policy Using Climate Data

Analysis of Indian Renewable Energy Policy Using Climate Data India’s high renewable target might be achievable but it comes at a significant cost, given the nation’s alreadystressed power system. That’s why it is important to get the policy design right from the beginning. The starting point should be a climate data analysis to assess the resource potential and its spatial and seasonal distribution. Mohar Chattopadhyay and Deb Chattopadhyay Mohar Chattopadhyay is a Research Scientist with the Commonwealth Scientific and Industrial Research Organisation. Her main interests are in regional climate modeling, tropical cyclones, and climate change. Deb Chattopadhyay is a Director with the Economics and Infrastructure Advisory practice of Deloitte Australia in Melbourne, Australia. He has been working on Indian power sector issues since 1990, including renewable energy policy and cross-border power trading in South Asia via his affiliation with the Asian Development Bank.

I. Introduction Renewable energy policies comprise a relatively new introduction to the power sectors of several countries around the world, including India, Australia, Japan, the U.S., and the UK and other European countries. Most of these policies have commenced around 2000 and have started making a significant impact on the power system in the last five years or so. In general, these policies have made a major difference in terms of penetration of wind- and more recently

solar-based power generation. However, the efficacy of these policies hinge critically on the design and choice of parameters that best fit a region/country. Any renewable energy policy should ultimately encourage efficient development of available resources. It needs to send the right signals to investors to develop the right type of resource at the right location at the right time. In this article, we have focused on the need for renewable policy design to be informed by sound climate data analysis in the Indian context. We have

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presented a high-level analysis for India to gauge whether the current set of renewable energy policies is meeting this end. e note that the merits of renewable energy policy have been debated quite extensively, as reflected in a number of articles published in The Electricity Journal since 1996. Wiser et al. [1] had, for instance, provided a thorough analysis of the renewable portfolio standard at the state level in the U.S. in 2007, concluding that the outcomes of RPS have at best been ‘‘mixed.’’ A separate renewable energy standard as part of a holistic climate change action plan has also been a hotly debated issue. Michaels [2], among others, has questioned the foundation for an RPS ‘‘when wide-ranging regulations, implementation plans, and emissions markets are in place, functioning reasonably well, and can be modified as new information arrives.’’ Similar argument has been leveled against the Mandatory Renewable Energy Target (MRET) policy in Australia [3]. Sovacool and Watts [4], on the other hand, argue that the entire power system of the U.S., New Zealand, and potentially many other countries could and should go 100 percent renewable. At the heart of these debates lies the fundamental issue of the choice of one resource over another in a particular region, i.e., ‘‘resource eligibility’’ and ‘‘geographical eligibility.’’ These debates continue to be raised in industry forums and in the academic literature alike. More

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than any specific commercial or political agenda, these debates probably show that renewable policies are still in the formative stage. There are potentially areas of enhancement and refinement that might allow these policies to be articulated in a way that maximizes their chance of success to promote only those resources that are technically and economically attractive. That said, these policies have already started

At the heart of these debates lies the fundamental issue of the choice of one resource over another in a particular region. mobilizing thousands, if not tens of thousands, of renewable MW capacity in some countries. More importantly, they are likely to be active for at least another decade with far greater capacity additions in the offing. Any sub-optimality in the policy design therefore is getting more expensive with every passing year and setting in longlived inefficient investments in the power sector. e have focused on India because it is one of the fastest-growing renewable energy markets in the world and takes the center stage of India’s National Action Plan on Climate Change.1 Solar power capacity in

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India, for instance, stood at just 3 MW in 2008, but has grown to approximately 46 MW in July 2011 and is targeted to reach 20,000 MW by 2022 [5]. According to Khattar [6], the second round of bidding for the first phase of Solar Mission projects in December 2011 attracted 1,915 MW of capacity. Wind power capacity in India has preceded the rush for solar capacity. Wind capacity stood at 5,341 MW in 2006 and has almost tripled to touch 15,000 MW in late 2011. There is almost $100 billion investment at stake in the Indian (Jawaharlal Nehru) National Solar Mission (JNNSM) alone over the coming 10 years. The total renewable capacity addition targeted over the next decade is 74,000 MW. If we include investment in wind power, which is the dominant form of renewable capacity in India at present, the total investment in solar and wind would be on the order of $180 billion over the next decade. These targets suggest a more rapid penetration of renewable technologies compared to other countries. If we compare the history of renewable energy targets in Australia, for example, there was a far more cautious approach in starting with a 2 percent target followed by a 9,500 GWh (approximately 4.5 percent) target before embarking on a 20 percent target by 2020. Unlike the Australian renewable program, which announced the next stage only after completing the previous one and reviewing its performance, Indian

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renewable policy has been quite aggressive in setting the longterm targets up front. While it has its benefits in providing certainty, one would also hope that the targets have a strong scientific and commercial foundation, and there is a clearly articulated strategy to get there. n the present research, we have provided a critical review of the renewable energy targets set by the Indian state and federal governments and, in particular, we have highlighted an important attribute that seems to be missing in Indian renewable policy design: namely, adequate consideration of spatial and seasonal distribution of solar and wind resources. The addition of significant amount of renewable capacity in India as well as overseas has started accumulating enough operational data to get better insights into the actual performance of renewable projects. However, these are, apart from its commercial confidentiality aspect, still inadequate to form a view on the efficacy of a long-term policy that will span the length and breadth of the country. More importantly, the policy parameters ought to be informed ahead of its implementation as best as possible, using a combination of historic climate data, pilot studies, and model forecasts. We have performed a high-level analysis using historic climate data from 1980 to 2010 to assess spatial and seasonal distribution of goodquality solar and wind resources in India. We have compared and contrasted the historic data against

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the targets. We have first provided an overview of the salient facets of the renewable energy policy, including the renewable targets, followed by our climate data analysis.

II. Current Renewable Energy Scenario in India Renewable energy policy in India has several elements that

There is a lack of uniformity in setting the RPO to date: some states chose to specify it by types of resource and others specified a total target. resemble its U.S. counterpart with its complex gamut of state and federal policies. At a high level, the renewable policy is enshrined in the National Action Plan on Climate Change to deliver 15 percent of total electrical energy in the country by 2020. A statebased Renewable Purchase Obligation (RPO) is the cornerstone of the policy in India that sets the target by geography and year for each state determined by the respective state commission. Overlaid on this are federal/national targets including a separate solar energy target. The genesis of the RPO is relatively recent, with the notion

first being introduced via the Electricity Act 2003, followed by the National Renewable Energy Policy in 2005, and the National Tariff Policy in 2006. Some states are still in the process of developing their own targets. There is a lack of uniformity in setting the RPO to date: some states chose to specify it by types of resource and others specified a total target. This lack of uniformity in part reflects the evolving nature of the policy framework, including the more recent addition of the solar policy. The National Solar Mission, i.e., the 20,000 MW target, is applied uniformly across all states – there is no differentiation in solar tariff across the states. Most states currently have a solar target of 0.25 percent of total energy that is expected to rise to 3 percent by 2020 in line with the national target. The Solar Mission comprises three phases: 1,000 MW of grid-connected solar power by 2013, 4,000 MW by 2017, and 20,000 MW by 2022. The timeframe for RPO also differs across the states. There are scant details on how the State Commissions arrived at the RPO, or whether and how it will be revised going forward. Table 1 summarizes the current level of RPO and provides some idea of the complexity and lack of uniformity of the renewable energy policy. The Central Electricity Regulatory Commission (CERC) and State Energy Regulatory Commission (SERC) publications [7] provide a detailed account of the renewable energy policy and RPO by state.

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Table 1: Renewable Purchase Obligation (% of Total Energy) for Indian States State 1

2

3

4

5

Gujarat

Maharashtra

Uttaranchal

Manipur

Mizoram

Renewable Resource/Total

2010–11

2011–12

2012–13

2013–14

2014–15

2015–16

Wind Solar

4.5% 0.25%

5.0% 0.5%

5.5% 1.0%

others Total

0.25% 5%

0.5% 6%

0.5% 7%

Solar Non-solar

0.25% 5.75%

0.25% 6.75%

0.25% 7.75%

0.50% 8.5%

0.50% 8.5%

0.50% 8.5%

Total

6%

7%

8%

9%

9%

9%

Solar

0.25%

0.5%

1.0%

Non-solar

3.75%

4.5%

5.0%

Total

4%

5%

6%

Solar

0.25%

0.25%

0.25%

Non-solar Total

1.75% 2%

2.75% 3%

4.75% 5%

Solar Non-solar

0.25% 4.75%

0.25% 5.75%

0.25% 6.75%

Total

5%

6%

7%

6

Jammu & Kashmir

Total

1%

3%

5%

7

Uttar Pradesh

Solar Non-solar

0.25% 3.75%

0.5% 4.5%

1% 5.0%

Total

4%

5%

6%

8

Tripura

Solar Total

0.1% 1%

0.1% 1%

0.1% 2%

9

Jharkhand

Solar Non-solar

0.25% 1.75%

0.5% 2.5%

1% 3.0%

Total

2%

3%

4%

Solar

0%

0.1%

0.1%

10

Himachal Pradesh

Non-solar Total 11

12

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Orissa

Assama

Solar Non-solar

10% 10.10%

11% 11.10

12.5% 12.10%

1.0%

0.10% 1.2%

0.15% 1.4%

0.20% 1.6%

0.25% 1.8%

0.30% 2%

Co-gen

3.50%

3.70%

3.95%

4.20%

4.45%

4.70%

Total

4.5%

5%

5.5%

6%

6.5%

7%

Solar

0.05%

0.1%

0.15%

0.2%

0.25%

Total

1.4%

2.8%

4.25

5.6%

7%

13

Tamil Nadu

Total

14%

14 15

Delhi Andhra Pradesh

Total Total

1% 5%

16

Karnataka

Total

11%

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Table 1 (Continued )

Renewable State

Resource/Total

2010–11

17

West Bengal

Total

10%

18 19

Rajasthan Madhya Pradesh

Total Total

9.5% 10%

20

Punjab

Total

4%

21

Haryana

Total

10%

a

2011–12

2012–13

2013–14

2014–15

2015–16

9.5%

Provisional target.

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ince the resource endowment varies enormously across the states, it was quickly realized that some form of trading of renewable energy is essential to provide the right level of incentive to develop a higher quantum in resource-rich states and trade it with other states. CERC had introduced the concept paper on renewable energy certificates (REC) trading in 2009, followed by the initial terms and conditions of tradeable RECs in January 2010. REC trading started on the existing power exchanges in India in March 2011. Table 2 shows the annual target for the current year and total renewable capacity built. Clearly, wind has remained the

forerunner among all renewable resources. That said, the annual targets for wind remain steep and are going to be steeper in the coming years. Solar capacity is well short of the current target. Notwithstanding the optimism in the latest round of bidding for capacity, the first phase target of 1,000 MW by 2013 is likely to pose a significant hurdle. More generally, the overall annual renewable target is hard to achieve and as future year targets grow steeper, it poses great challenges. The emerging gap in demand and supply is also manifested in REC trading volume and prices. Since the start of the significant level REC trading on Indian Energy

Table 2: Renewable Energy Capacity (MW) Target for 2011–12

Build to Date in 2011–12

Cumulative Capacitya

2,400 350

833 111

14,989 3,154

Biomass Bagasse Cogeneration

460

86 111

1,084 1,779

Waste to Power-Urban

25

1

20

200

– 8

53 46

3,435

1,152

21,126

Renewable Resource Wind Small Hydro

Waste to Power-Industrial Solar Power (SPV) Total

Source: Ministry of New and Renewable Energy (MNRE) January 2011. a As of 31 August 2011.

Exchange (IEX) in July 2011, REC trading volume has more than tripled over July–December 2011. REC prices have doubled from Indian Rupees (Rs) 1,500/MWh (about $30/MWh) in July to close to Rs 3,000 ($60/MWh) in December 2011 with the number of buy bids exceeding sell bids by 60 percent.2 s already noted, a major weakness of the current renewable energy policy is that it does not provide clear guidance on how the RPOs should be set to achieve the best possible mix of renewables, taking into account seasonal variation of these resources. It is a significant issue that can potentially derail the renewable development in the country. The strategic plan recently issued by the Ministry of New and Renewable Energy (MNRE), the ministerial body in charge of development of policy and implementation in the country [8], for instance, heavily focuses on implementation of the renewable targets. To the best of our knowledge, there has been no conscious attempt by MNRE, or state agencies, to reassess the targets in light of the constraints that are being faced and/or enhanced resource database and

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information that have become available since the policy was originally formulated. MNRE has dedicated research bodies including a Solar Energy Centre and a Centre for Wind Energy Technology that prima facie look at technology implementation aspects. That said, we understand that there are some research activities being initiated in collaboration with overseas research bodies such as NREL and RISO, Denmark, to develop resource maps and collect observations from automated weather stations.3 n summary, the Indian renewable energy sector seems to be showing the early signs of a stressed market condition, which in our view is largely an artifact of the renewable policy design. The experience to some extent reflects the early phase of policy development in the U.S. and Australia, albeit the developments in India is moving at a faster pace to achieve what seems to be a steep target. We also note that the opportunity cost of investment in a power sector that is struggling to keep up with demand is extremely high. It is putting significant pressure on governments and utilities to buy solar power at a price that is more than double the regular tariff.4 For instance, $180 billion could be deployed to develop 120,000–150,000 MW of conventional power stations including high-efficiency baseload (combined cycle and open cycle) gas-based capacity. This will provide for bulk of the new capacity requirement for the entire

power sector over the next decade. In fact, if renewable policy is not properly designed to deal with some of the seasonal variability issues, a reasonably significant part of conventional generation capacity, possibly in the form of peaking gas turbines, would be needed in any case to cater to seasonal variation of wind/solar/ hydro.

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In the next section, we have presented a high-level analysis of climate data covering the last 31 years. We have done a qualitative assessment of the resource potential revealed by the climate data against the renewable targets.

III. Analysis of Climate Data In this section, we have discussed the geographical and seasonal variation of wind and solar resources in India. There seems to be surprisingly little analysis of climate data that has gone into setting the policy parameters (namely, resource and

geographical eligibility and RPO). Although there are several climate data sources that are available, it is remarkable that the first comprehensive study on solar potential for India that used climate data became available as late as 2009 as part of a global study on concentrating solar power potential by Trieb et al. [9]. In 2010, the U.S. Department of Energy funded a project in collaboration with MNRE to develop a Solar Resource Map that used 10 km resolution data of Direct Normal Irradiance (DNI) over 2002–08.5 NASA’s Surface Meteorology and Solar Energy (SSE) dataset is another solar data source that has been cited in one of the recent reviews of renewable energy status in India [10].6 Again, it is hard to see whether any of these, or other available, datasets has been used in the policy decision-making or reassessment of renewable targets. Our review of the policy documents including some of the key ones from MNRE and CERC that we have cited [7,8] does not reveal any immediate connection. We have noted that there is readily available a significant amount of historic climate data covering the last 31 years. One can form a reasonable view on distribution of renewable resources and its seasonality from this data. These factors in our view are vital to assess and inform policy decisions such as renewable targets and geographical and resource eligibility so that an economically viable and balanced resource mix can be achieved in

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e have used the ERAInterim data from January 1980 to December 2010 to calculate annual and monthly mean of downward solar radiation, 10 m wind speed, total precipitation, and runoff. The specifications of the data used are given below:  Downward solar radiation (SSRD): 12-hour forecast data for 0 UTC analysis period. We have converted this data to equivalent annual energy intensity and daily average intensity.  10 m wind speed (U10): Monthly mean of daily mean 10 m wind speed. We have calculated the wind power density at 10 m assuming a Rayleigh distribution of wind speed and air density of 1.23 kg/m.3  Total precipitation and runoff: 12-hour forecast data for 0 UTC analysis period. he spatial and seasonal distribution of annual solar power, wind power and precipitation data are shown in Figures 1–8. We recognize that the

the long run. The scope of our analysis is, however, limited. Indeed, it does not substitute for high-resolution data, measurement, modeling and forecasting, and detailed regional/local case studies, which would all add value in refining these views.

IV. Climate Data Employed The climate data used for analysis in this study are European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis data (‘‘ERA data’’) at a horizontal resolution of 1.58  1.58 [11]. ERAInterim is the latest reanalysis product from ECMWF which has made considerable improvements against the earlier product ERA40. Dee et al. [11] provides a detailed description of the various observational products used as well as the four-dimensional data assimilation technique, and the forecast model used in creating the re-analysis data. This data has been used extensively that includes, inter alia: 1. Szczypta et al. [12], to study the hydrological cycle over France; 2. Hodges et al. [13] used it as a comparator for their study and found the product to be comparable or better than the reanalysis data available. 3. Menkes et al. [14], who have recently compared ERA-Interim data against other reanalysis products available; and

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resolution of the data is coarse. Nevertheless, it is adequate for our purpose to show broad trends of resource distribution. We have also checked that using a better (0.758  0.758) resolution yields very similar outcomes. Figure 1 shows solar insolation data for areas that exceed approximately 2,000 kWh/sqm/ year. Since the commercial viability of large-scale solar installations (e.g., using concentrating solar power technology that generally offers better economics than PV) drop considerably below an annual solar radiation of 2,000, we have included areas that have solar radiation of 1,980 or higher.7 It is immediately obvious that good quality solar resources that are commercially viable in the near term are confined to a relatively small part of the country – mostly in Western/Northwestern and Southeast India. Indeed, 100 percent of the solar bids in the latest round are located in these

[(Figure_1)TD$IG]

Figure 1: Annual Average Solar Insolation >2000 kWh/sqm/day (1980–2010)

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[(Figure_2)TD$IG] longer timeframe (31 years as opposed to 7 years) seems to make a reasonably significant difference. n important consideration for renewable resources is its seasonal variability. We have selected a few locations to show the monthly resource variability. Figure 3 shows the locations for solar (S1 and S2) as well as wind (W1 and W2) and precipitation (R1 and R2) data. Figure 4 shows seasonal/ monthly and spatial (across two locations in Northwest and Southern) variation of solar radiation. Solar energy availability in winter is approximately half of its potential in summer. There is also some difference in monthly profile across the locations, albeit the spatial variability is far less prominent than the seasonal counterpart. These observations are of great significance from a power system security perspective. It implies, for

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Figure 2: Daily Average Solar Insolation >5 kWh/sqm/day (2002–08)

states (namely Rajasthan, Maharashtra, and Tamil Nadu). It is unclear why a national solar mission has imposed a uniform (long-term) target of up to 3 percent of total energy for all states. The target by 2020–21 translates into more than 30,000 GWh of total solar-based electricity. Although it is predicted solar energy will achieve grid-parity by then, it does impose a heavy financial burden on purchasers in the short to medium term. A difference in solar radiation of 1,800 and 2,000 kWh/sqm/year, for instance, adds 11 percent to the cost of solar power. If half of the annual solar target in 2020 needs to be met at a sub-optimal level of solar radiation, it will add close to half a billion dollars per year in additional bills that the REC purchasers would need to pay.8 reasonably significant data issue that we should note here relates to the timeframe of historic climate data. A number of sources in India including Arora

et al. [10] cite the high-resolution (10 km) NREL data that covers the period 2002–08, and shows a significantly better radiation profile (in excess of 5 kWh/sqm/ day) for most locations in India. If we restrict the timeframe of the ERA dataset to 2002–08, we get a very similar profile to the NREL data as Figure 2 demonstrates.9 While the NREL data is of much higher resolution, inclusion of a

[(Figure_3)TD$IG]

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Figure 3: Selected Locations to Show Wind (W), Solar (S) and Precipitation Seasonality (R)

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[(Figure_4)TD$IG]

Figure 4: Seasonal and Spatial Variation: Solar Radiation (daily average)

standards, even these sites would be classified as bordering ‘‘good,’’ rather than ‘‘excellent.’’ India has, however, achieved almost 15,000 MW of wind generation capacity development with Tamil Nadu (Southern India) accounting for more than 6,000 MW wind capacity. It remains to be seen, though, if India can achieve the additional 50,000 MW target for wind by 2020 that has been drafted as part of the National Climate

[(Figure_5)TD$IG] instance, the need for capacity/ energy reserve to counter the seasonal variation would be massive, especially if the swing in solar energy accounts for up to 15,000 GWh per year in 2020. The additional cost to supplant intermittent renewable resources is not an arcane point today as power systems in a number of countries are already beginning to incur them. Nonetheless, the sheer magnitude of these costs for a 20,000 MW National Solar Mission calls for a more rigorous analysis that is conspicuous in absence. Figures 5 and 6 show the distribution of wind power density (WPD) at 10 m height, and its seasonal/monthly variation, respectively. WPD has been estimated by NREL to be close to double at a 50-m height, but we do not have access to historic climate ERA data at that height. At 100 watt/sqm (10 m) or say 200 watt/sqm (50 m) – commercially viable wind sites are located mostly around the coastline and a few other parts of Southern India. By most

Figure 5: Distribution of Annual Mean Wind Power Density (watt/sqm): 1980–2010

[(Figure_6)TD$IG]

Figure 6: Seasonal and Spatial Variation: Wind Power Density (watt/sqm)

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Change Action Plan, although there are other estimated wind targets that suggest a lower target of 40,000 MW by 2022 [10]. This implies on average 4,000 MW of wind capacity needs to be added every year for next nine to 11 years. ince the majority of the good sites are already taken up, there has been a slackening of capacity addition despite very good incentives provided under the Generation Based Incentive (GBI) scheme that provides up to $120,000 per MW to wind producers. Wind capacity addition in 2010–11 was 1,376 MW and there has been less than 1,000 MW addition in 2011–12 to date. MNRE’s own estimate of total good-quality wind power potential (i.e., above 200 watt/ sqm) is just below 50,000 MW. Since our data suggests even lower wind power density, the feasibility of achieving an additional 50,000 MW of high-quality wind resource is an area that is well worth investigating further. The seasonal distribution of wind shows the WPD is in the range of 200–300 watt/sqm in good sites during the monsoon months, but a typical low-quality wind site (W2) is very unlikely to meet the commercial requirements even after considering the full financial subsidies offered by the government. We also note that both solar and wind resources have a strong correlation as far as seasonal energy variability is concerned. While such correlation has some positive aspects to supplant fossil fuel generation during May–September,

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especially as coal supply during the monsoon is often hindered, it also implies the need for significant backup power generation capacity during other months of the year. A total wind capacity to the tune of 65,000 MW can have a profound impact on the power system. This was foreshadowed by Singh and Singh [15], and we are aware of various

states currently undertaking power evacuation studies to assess the cost of transmission upgrades. Nevertheless, it is a significant cost that ought to have been considered up front to decide the appropriate level of RPO for each state. n the other hand, there is significant untapped hydro potential in India that does not feature as prominently in the latest renewable policy and accounts for less than 10 percent of the targeted renewable mix in 2022 [7,8,10]. An ADB study in 2007 [16] had estimated that out of 84,000 MW of good-quality hydro potential (at 60 percent capacity factor) only 20 percent had been developed as of 2006. In 2003, an initiative to develop 50,000 MW at a low tariff

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of Rs 2.50/kWh (in 2003 Rupees that is equivalent to about $54 per MWh) was initiated by the prime minister of India that included 73 projects. More than 90 percent of these projects are run-off-the-river schemes reliant on precipitation, and are located in the Brahmaputra, Indus, and Ganga basins in the north and northeastern part of the country. The northeastern region alone accounted for an aggregate capacity of 30,416 MW, the majority of which can be developed under a levelized cost of $50 per MWh. Some of these projects have since been implemented or are under construction, including the 2,000 MW Lower Subansiri project, although it is not clear the extent to which the original 2003 scheme of 73 projects will be integrated into the current renewable strategy. Figure 7 shows the annual average mean precipitation over 1980–2010 that provides good support in favor of this targeted hydro development initiative. The northeastern region has one of the highest rates of precipitation in the world. The precipitation data in the figure also alludes to the fact that the adjoining areas to northeastern India, which include Bhutan and Nepal, are likewise endowed with very significant hydro resources. There is in fact a short- to medium-term hydro power potential in excess of 20,000 MW by 2020 in Bhutan and 40,000 MW in Nepal that can also be developed at relatively low cost. Cross-border power trading presents a great opportunity to

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[(Figure_7)TD$IG]

Figure 7: Annual Average Mean Precipitation (mm) 1980–2010

meet soaring power demand in India [17]. Together with the hydro projects in India and of course significant interconnection and upgrade of the grid, can unleash good quality renewable resources to meet the aggregate RPO of the nation by 2020 at a fraction of the cost of the solar and wind projects. inally, Figure 8 shows the seasonality of precipitation in the Northeast as well as that for a location in western India with lower rainfall. Hydro power naturally shows a very high degree of seasonality that can to some extent be countered through storage hydro projects, albeit at a higher cost. It is important to note that the seasonal variation of solar as well as wind coincides with hydro, with a low energy yield during the winter months. In other words, solar and wind energy do not provide a seasonal energy backup for hydro, leaving the onus for additional generation during the winter months to some other renewable, or nonrenewable, resource.10

V. Concluding Remarks

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India has embarked on a rapidpaced development of 74,000 MW of additional renewable resources over the next decade, including 20,000 MW of solar power and somewhere between 40,000 and 50,000 MW of wind. It is undoubtedly a welcome move from several perspectives, as noted in the policy statements. Given the sheer magnitude of the tasks and investments at stake, it

[(Figure_8)TD$IG]

is important that these objectives are backed up by a policy design that delivers an efficient and balanced outcome. A high renewable target, even an unreasonably high one, can possibly be met, but it comes at a significant cost, which for an already stressed power system in India can be seriously bad news. It is, therefore, important to get the policy design right from the beginning. The starting point for such a design should be a climate data analysis to assess the resource potential and its spatial and seasonal distribution. A ‘‘climate-informed’’ policy would go a long way to ensure that the key parameters around target and resource and geographical eligibility criteria are set sensibly. The policy needs to retain sufficient degree of flexibility so that these parameters can be finetuned as actual performance data and updated/enhanced forecasts become available. We have undertaken a high level assessment of the renewable

Figure 8: Seasonal and Spatial Variation: Precipitation

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energy policy using historic climate data. It is limited and amounts to nothing more than a ‘‘smell test’’ to see if the parameters broadly align with what past three decades data suggest. Our analysis comes up with more questions than answers. We do not find sufficient evidence to suggest a 20,000 MW, or 3 percent Renewable Purchase Obligation (RPO) for all states, by 2020 can be met using good quality solar resource (i.e., greater than 2,000 kWh/sqm/year). Similarly, we do not find sufficient land areas with good quality wind resource. It does not conform to the need for additional 40,000–50,000 MW of wind capacity to be developed by 2020 to meet the obligation under the National Action Plan on Climate Change. Also, the significant seasonal variability of these two targeted resources over the months and a strong correlation between them, should be a concern from a power system planning and operation perspective. Imposing a unilaterally high standard for solar/renewable across the board is likely to cause an increasing level of financial hardship for the buyers, and promote inefficient renewable resource development. It is unclear the extent to which the policy design was informed by climate data. We have found little supporting analysis although we do note that there are some recent efforts being put in place by the Ministry of New and Renewable Energy and the Central Energy Regulatory Commission. 92

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leaner target and resourcespecific renewable policy are likely to better serve the objectives of the nation. Ideally, it should also encompass broader options such as cross-border power trading with Bhutan and Nepal to meet the renewable purchase obligations, e.g., by allowing generators in these countries to trade REC on the

Indian power exchanges. There is also some good news, namely (a) Some elements of the overall renewable strategy developed in the past account for resourcecentric development including the north-eastern hydro development that was initiated in 2003. (b) The renewable strategy is in a formative stage with the RPOs still being determined for some states; and (c) Most importantly, the policy framework does have sufficient flexibility built through staged development and a continual review of targets. There has been a modest beginning to climate data analysis in the Indian context including the work being carried out by the

research wing of MNRE. Let us hope that the policy makers are better informed through this and other supporting data collection efforts, and eventually amend the policy parameters to achieve a more efficient outcome.&

References [1] R. Wiser, C. Namovicz, M. Gielecki and R. Smith, The Experience with Renewable Portfolio Standards in the United States, Elec. J., May 2007 at 8–20. [2] R. Michaels, Renewable Portfolio Standards: Still No Good Reasons, Elec. J., Oct. 2008 at 18–31. [3] D. Chattopadhyay, Modeling Greenhouse Gas Reduction from the Australian Electricity Sector, IEEE Trans. Power Syst., Feb. 2010 at 729–740. [4] B.K. Sovacool and C. Watts, Going Completely Renewable: Is It Possible (Let Alone Desirable)? Elec. J., May 2009 at 95–111. [5] R. Lohia Verma, Evolution of Solar Power in India, presented at ICEM Conference, Gold Coast, Nov. 2011, at http://www. icem2011.org/presentations2011/ 5_Friday/4A/1215_Lohia.pdf [6] D. Khattar, Price Surprise: JNNSM Batch II Bidding Yields Unexpectedly Low Prices, Renewable Watch, Dec. 2011. [7] Central Energy Regulatory Commission, Implementation of the Renewable Energy Certificate Mechanism, Jan. 2012, at http:// www.nldc.in/REC.aspx [8] Ministry of New and Renewable Energy, Strategic Plan for New and Renewable Energy Sector for 2011– 2017, Feb. 2011, at http://www. mnre.gov.in/policy/strategicplan-mnre-2011-17.pdf [9] F. Trieb, et al., Global Potential of Concentrating Solar Power, in: SolarPaces Conference, Berlin, 2009. [10] D.S. Arora, et al., Indian Renewable Energy Status Report: Background Report on Delhi International Renewable Energy

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[11]

[12]

[13]

[14]

[15]

[16]

[17]

Conference, NREL/TP-6A2048948, Oct. 2010. D.P. Dee, April 2011, et al., The ERA-Interim Reanalysis: Configuration and Performance of the Data Assimilation System, Q.J. Royal Meteorological Soc., Part A, 137 (656) at 553–597. C. Szczypta, J.-C. Calvet, C. Albergel, G. Balsamo, S. Boussetta, D. Carrer, S. Lafont and C. Meurey, 2011, Verification of the New ECMWF ERA-Interim Reanalysis over France, Earth Sys. Sci., 15 at 647–666. K.I. Hodges, R.W. Lee and L. Bengtsson, 2011, A Comparison of Extratropical Cyclones in Recent Reanalyses ERA-Interim, NASA MERRA, NCEP CFSR, and JRA25, J. Clim., 24 at 4888–4906. C.E. Menkes, 2011, et al., Comparison of Tropical Cyclogenesis Indices on Seasonal to Interannual Timescales, Clim. Dyn., 38 (1–2) at 301–321. B. Singh and S.N. Singh, Wind Power Interconnection into the Power System: A Review of Grid Code Requirements, Elec. J., June 2009 at 54–63. Asian Development Bank, Hydropower Development in India: A Sector Assessment, 2007. D. Chattopadhyay and P.N. Fernando, Cross Border Power Trading in South Asia: It’s Time to Raise the Game, Elec. J., Nov. 2011at 41–50.

Endnotes: 1. See for instance the statement made by Jayanthi Natarajan, India’s Minister of Environment and Forest, at the Durban Conference of Parties (COP17), at http://unfccc.int/files/ meetings/durban_nov_2011/ statements/application/pdf/ 111207_cop17_hls_india.pdf. 2. IEX currently accounts for 95 percent of REC trading in India. REC trading volume in Dec. 2011 crossed 100 GWh. REC volume and prices are available at: http:// www.iexindia.com/IEXPower/ Uploads/PressRelease/2011/ December/Press%20ReleaseIEX%20REC%2028th%20Dec.pdf.

3. The recent research efforts at MNRE include a collaboration with NREL to develop a solar resource map in 2009–10. MNRE has also just initiated a major project on Solar Radiation Resource Assessment to measure solar radiation at 51 sites throughout the country. MNRE’s Centre for Wind Energy Technology (CWET) has a collaboration with RISO, Denmark, to develop a numerical wind atlas. CERC’s review of performance of solar power plants has also identified the need for goodquality solar radiation data and makes an initial effort to assemble model data from multiple sources. However, these efforts have been limited, focused on operational issues and implementation, and hence do not seem to have had much impact on the policy parameters to date. Further information on the solar study undertaken by NREL for MNRE is available at: http://www.mnre. gov.in/sec/National%20Renewable% 20Energy%20Laboratory/contents/ pdfs/india_solar_methods_final.pdf. Further information on CWET is available at: http://www.cwet.tn. nic.in/html/departments_numerical_ wind_atlas.html. CERC’s review of solar power plants is also available online: http://www.cercind.gov.in/ 2011/Whats-New/PERFORMANCE %20OF%20SOLAR%20POWER% 20PLANTS.pdf. 4. A recent report from KPMG titled The Rising Sun provides an extensive review of the solar pricing policy. KPMG has estimated that solar power will achieve ‘‘grid parity’’ by 2019-20. CERC had also noted in its review of solar power plants in Feb. 2011 that grid parity may be achieved over 2017–20. The KPMG report is available at: http://www.kpmginstitutes.com/ global-energy-institute/insights/ 2011/pdf/the-rising-sun-may2011.pdf. 5. The NREL Solar Resource Map is available at: http://www.nrel.gov/ international/ra_india.html. 6. The SSE dataset can be accessed via the SWERA Renewable Energy Resource Explorer GIS analysis tool: http://na.unep.net/swera/ index.php?id=7.

7. IEA Technology Roadmap for Concentrating Solar Power, published in 2010, estimates the levelized cost of solar power around $300/MWh in 2010, but is expected to drop around $150/MWh by 2020. The IEA report is available at: http://www.iea.org/ papers/2010/csp_roadmap.pdf. In comparison, the recent rounds of bidding for solar power in India (in Oct. 2011 and Dec. 2011) have already revealed lower prices around $200/ MWh on average. Since CSP is cheaper than PV, it seems unlikely that areas with an annual DNI well below 2,000 will yield a commercially viable outcome in the near term unless solar costs fall more rapidly than IEA had estimated in late 2010. In the latest round of bidding, 23 out of 27 projects accounting for 87 percent of the total capacity were selected in Rajasthan (Western part of India) which has the highest solar radiation with DNI well in excess of 2,000. 8. Smaller-scale off-grid solar projects and solar power generation in lowsolar-intensity areas would increase the costs substantially. Solar home lighting kits (37 watt) in Northeastern India have been estimated to have a levelized cost of approximately $1,000 per MWh due to a combination of its micro-scale and the poor solar radiation in the region. Solar home lighting has been an integral part of the rural electrification schemes in India regardless of location. This is another example of a policy that could be better informed using climate data. 9. A NREL resource map showing daily average solar radiation is available online: http:// www.nrel.gov/international/ images/dni_annual.jpg. 10. Solar and wind may be complementary resources for a shorter timescale such as intra-day, which is valuable from a power system perspective. However, seasonal variability in energy is an important consideration that contributes to the need for additional capacity, especially as renewable resources become a significant (15 percent or more) part of the total energy supply.

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